Acoustic Event Classification Using Spectrogram Features

Manjunath Mulimani; Shashidhar G. Koolagudi

doi:10.1109/TENCON.2018.8650444

Acoustic Event Classification Using Spectrogram Features

Manjunath Mulimani, Shashidhar G. Koolagudi

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

24 Citations (Scopus)

Abstract

This paper investigates a new feature extraction method to extract different features from the spectrogram of an audio signal for Acoustic Event Classification (AEC). A new set of features is formulated and extracted from local spectrogram regions named blocks. The average recognition performance of proposed spectrogram based features and Mel-frequency cepstral coefficients (MFCCs) with their deltas and accelerations on Support Vector Machines (SVM) is compared. In this work, different categories of acoustic events are considered from the Freiburg-106 dataset. Proposed features show significantly improved performance over conventional Mel-frequency cepstral coefficients (MFCCs) for Acoustic Event Classification.

Original language	English
Title of host publication	Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1460-1464
Number of pages	5
ISBN (Electronic)	9781538654576
DOIs	https://doi.org/10.1109/TENCON.2018.8650444
Publication status	Published - 22-02-2019
Event	2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of Duration: 28-10-2018 → 31-10-2018

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume	2018-October
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2018 IEEE Region 10 Conference, TENCON 2018
Country/Territory	Korea, Republic of
City	Jeju
Period	28-10-18 → 31-10-18

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TENCON.2018.8650444

Cite this

Mulimani, M., & Koolagudi, S. G. (2019). Acoustic Event Classification Using Spectrogram Features. In Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference (pp. 1460-1464). Article 8650444 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2018-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2018.8650444

@inproceedings{6d548a73b9174d639dc335448b78a59f,

title = "Acoustic Event Classification Using Spectrogram Features",

abstract = "This paper investigates a new feature extraction method to extract different features from the spectrogram of an audio signal for Acoustic Event Classification (AEC). A new set of features is formulated and extracted from local spectrogram regions named blocks. The average recognition performance of proposed spectrogram based features and Mel-frequency cepstral coefficients (MFCCs) with their deltas and accelerations on Support Vector Machines (SVM) is compared. In this work, different categories of acoustic events are considered from the Freiburg-106 dataset. Proposed features show significantly improved performance over conventional Mel-frequency cepstral coefficients (MFCCs) for Acoustic Event Classification.",

author = "Manjunath Mulimani and Koolagudi, \{Shashidhar G.\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Region 10 Conference, TENCON 2018 ; Conference date: 28-10-2018 Through 31-10-2018",

year = "2019",

month = feb,

day = "22",

doi = "10.1109/TENCON.2018.8650444",

language = "English",

series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference",

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}

Mulimani, M & Koolagudi, SG 2019, Acoustic Event Classification Using Spectrogram Features. in Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference., 8650444, IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2018-October, Institute of Electrical and Electronics Engineers Inc., pp. 1460-1464, 2018 IEEE Region 10 Conference, TENCON 2018, Jeju, Korea, Republic of, 28-10-18. https://doi.org/10.1109/TENCON.2018.8650444

Acoustic Event Classification Using Spectrogram Features. / Mulimani, Manjunath; Koolagudi, Shashidhar G.
Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1460-1464 8650444 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Acoustic Event Classification Using Spectrogram Features

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PY - 2019/2/22

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N2 - This paper investigates a new feature extraction method to extract different features from the spectrogram of an audio signal for Acoustic Event Classification (AEC). A new set of features is formulated and extracted from local spectrogram regions named blocks. The average recognition performance of proposed spectrogram based features and Mel-frequency cepstral coefficients (MFCCs) with their deltas and accelerations on Support Vector Machines (SVM) is compared. In this work, different categories of acoustic events are considered from the Freiburg-106 dataset. Proposed features show significantly improved performance over conventional Mel-frequency cepstral coefficients (MFCCs) for Acoustic Event Classification.

AB - This paper investigates a new feature extraction method to extract different features from the spectrogram of an audio signal for Acoustic Event Classification (AEC). A new set of features is formulated and extracted from local spectrogram regions named blocks. The average recognition performance of proposed spectrogram based features and Mel-frequency cepstral coefficients (MFCCs) with their deltas and accelerations on Support Vector Machines (SVM) is compared. In this work, different categories of acoustic events are considered from the Freiburg-106 dataset. Proposed features show significantly improved performance over conventional Mel-frequency cepstral coefficients (MFCCs) for Acoustic Event Classification.

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M3 - Conference contribution

AN - SCOPUS:85063197606

T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON

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BT - Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference

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Y2 - 28 October 2018 through 31 October 2018

ER -

Acoustic Event Classification Using Spectrogram Features

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